Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either "high risk" or "low risk" in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model. Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound
Objectives-This study was aimed to assess the ultrasound (US) characteristics of mycosis fungoides (MF)/Sézary syndrome (SS) and explore the value of high-frequency US in accuracy staging for classic mycosis fungoides (cMF). Methods-A prospective study was designed. Twenty-six patients with histopathologically confirmed MF or SS were enrolled to undergo HF-US examinations. Both 50-and 20-MHz US images of the most prominent lesion of each patient were collected by a cutaneous diagnostic US system, and the US characteristics in different stages were analyzed by 2 physicians independently. The Fisher exact test was used for the statistical analysis. Results-A total of 26 patients underwent this study, including 23 with cMF, 2 with folliculotropic mycosis fungoides (FMF), and 1 with SS. Among cMF, 16 patients with patches or plaques (the early-stage group) showed a subepidermal low-echogenic band, and only 3 lesions in the plaque stage partially extended to the superficial dermis. Seven patients with tumors (the advancedstage group) showed lesions that infiltrated to the deep dermis or subcutaneous tissue. The infiltration depth (P < .001), clarity of the boundary (P = .002), and homogeneity of internal echoes (P = .001) were significantly different between the early and advanced stages. Additionally, the 2 FMF lesions and 1 SS lesion had characteristic manifestations, showing a well-defined subepidermal lowechogenic band with patchy hypoechoic regions around the hair follicles in the dermis. Conclusions-High-frequency US can be used to accurately detect the infiltration depth and morphologic features of MF/SS lesions and provide important information for tumor staging of cMF. Additionally, the characteristic US features in FMF and SS might be helpful for diagnosis. Key Words-cutaneous lymphoma; high-frequency ultrasound; mycosis fungoides; Sézary syndrome M ycosis fungoides (MF) and Sézary syndrome (SS) are typical types of cutaneous T-cell lymphoma (CTCL). Mycosis fungoides and its variants are the most common types of CTCL, approximately accounting for 60% of cases. On clinical assessment, classic mycosis fungoides (cMF) lesions can be classified into patch, plaque, and tumor stages. Patients with SS commonly have rapidly progressing erythroderma with generalized lymphadenopathy. Accurate staging is an important prerequisite for individualized treatment of patients with MF. At present, skin lesions are classified into different stages mainly through inspection and
Background Accurate estimation of fetal weight is important for prenatal care and for detection of fetal growth abnormalities. Prediction of fetal weight entails the indirect measurement of fetal biometry by ultrasound that is then introduced into formulae to calculate the estimated fetal weight. The aim of our study was to evaluate the accuracy of fetal weight estimation of Chinese fetuses in the third trimester using an automated three-dimensional (3D) fractional limb volume model, and to compare this model with the traditional two-dimensional (2D) model. Methods Prospective 2D and 3D ultrasonography were performed among women with singleton pregnancies 7 days before delivery to obtain 2D data, including fetal biparietal diameter, abdominal circumference and femur length, as well as 3D data, including the fractional arm volume (AVol) and fractional thigh volume (TVol). The fetal weight was estimated using the 2D model and the 3D fractional limb volume model respectively. Percentage error was defined as (estimated fetal weight - actual birth weight) divided by actual birth weight and multiplied by 100. Systematic errors (accuracy) were evaluated as the mean percentage error (MPE). Random errors (precision) were calculated as ±1 SD of percentage error. The intraclass correlation coefficient (ICC) was used to analyze the inter-observer reliability of the 3D ultrasound measurements of fractional limb volume. Results Ultrasound examination was performed on 56 fetuses at 39.6 ± 1.4 weeks’ gestation. The average birth weight of the newborns was 3393 ± 530 g. The average fetal weight estimated by the 2D model was 3478 ± 467 g, and the MPE was 3.2 ± 8.9. The average fetal weights estimated by AVol and TVol of the 3D model were 3268 ± 467 g and 3250 ± 485 g, respectively, and the MPEs were − 3.3 ± 6.6 and − 3.9 ± 6.1, respectively. For the 3D TVol model, the proportion of fetuses with estimated error ≤ 5% was significantly higher than that of the 2D model (55.4% vs. 33.9%, p < 0.05). For fetuses with a birth weight < 3500 g, the accuracy of the AVol and TVol models were better than the 2D model (− 0.8 vs. 7.0 and − 2.8 vs. 7.0, both p < 0.05). Moreover, for these fetuses, the proportions of estimated error ≤ 5% of the AVol and TVol models were 58.1 and 64.5%, respectively, significantly higher than that of the 2D model (19.4%) (both p < 0.05). The inter-observer reliability of measuring fetal AVol and TVol were high, with the ICCs of 0.921 and 0.963, respectively. Conclusion In this cohort, the automated 3D fractional limb volume model improves the accuracy of weight estimation in most third-trimester fetuses. Prediction accuracy of the 3D model for neonatal BW, particularly < 3500 g was higher than that of the traditional 2D model.
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